Prognosis Of ccRCC Research Articles

Excavation of diagnostic biomarkers and construction of prognostic model for clear cell renal cell carcinoma based on urine proteomics.

Clear cell renal cell carcinoma (ccRCC) is the most common pathology type in kidney cancer. However, the prognosis of advanced ccRCC is unsatisfactory. Thus, early diagnosis becomes one of the most important research priorities of ccRCC. However, currently available studies about ccRCC lack urine-related further studies. In this study, we applied proteomics to search urinary biomarkers to assist early diagnosis of ccRCC. In addition, we constructed a prognostic model to assist judge patients' prognosis. Urine which was used to perform 4D label-free quantitative proteomics was collected from 12 ccRCC patients and 11 non-tumor patients with no urinary system diseases. The urine of 12 patients with ccRCC confirmed by pathological examination after surgery was collected before operatoin. Bioinformatics analysis was used to describe the urinary proteomics landscape of these patients with ccRCC. The top ten proteins with the highest expression content were selected as the basis for subsequent validation. Urine from 46 ccRCC patients and 45 control patients were collected to use for verification by enzyme linked immunosorbent assay (ELISA). In order to assess the prognostic value of urine proteomics, a prognostic model was constructed by COX regression analysis on the intersection of RNA-sequencing data in The Cancer Genome Atlas (TCGA) database and our urine proteomic data. 133 proteins differentially expressed in the urinary samples were found and 85 proteins (Fold Change, FC>1.5) were identified up-regulated while 48 down-regulated (FC<0.5). Top 10 proteins including S100A14, PKHD1L1, FABP4, ITIH2, C3, C8G, C2, ATF6, ANGPTL6, F13B were performed ELISA to verify. The results showed that PKHD1L1, ANGPTL6, FABP4 and C3 were statistically significant (P<0.05). We performed multivariate logistic regression analysis and plotted a nomogram. Receiver operating characteristic (ROC) curve indicted that the diagnostic efficiency of combined indicators is satisfactory (Aare under curve, AUC=0.835). Furthermore, the prognostic value of the urine proteomics was explored through the intersection between urine proteomics and TCGA RNA-seq data. Thus, COX regression analysis showed that VSIG4, HLA-DRA, SERPINF1, and IGLV2-23 were statistically significant (P<0.05). Our study indicated that the application of urine proteomics to explore diagnostic biomarkers and to construct prognostic models of renal clear cell carcinoma is of certain clinical value. PKHD1L1, ANGPTL6, FABP4 and C3 can assist to diagnose ccRCC. The prognostic model constituted of VSIG4, HLA-DRA, SERPINF1, and IGLV2-23 can significantly predict the prognosis of ccRCC patients, but this still needs more clinical trials to verify.

NUF2 is correlated with a poor prognosis and immune infiltration in clear cell renal cell carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is one of the most common malignancies. Recently, immunotherapy has been considered a promising treatment for metastatic ccRCC. NUF2 is a crucial component of the Ndc80 complex. NUF2 can stabilize microtubule attachment and is closely related to cell apoptosis and proliferation. This research is dedicated to investigating the role of NUF2 in ccRCC and the possible mechanisms.MethodsFirst, analysis of NUF2 mRNA expression levels in ccRCC and normal tissues by The Cancer Genome Atlas (TCGA) database and further verified by analysis of independent multiple microarray data sets in the Gene Expression Omnibus (GEO) database. Moreover, we evaluated and identified correlations between NUF2 expression, clinicopathologic variable, and overall survival (OS) in ccRCC by various methods. We investigated the relationship between NUF2 and tumor immune infiltration and the expression of corresponding immune cell markers via the Gene Expression Profiling Interactive Analysis (GEPIA) and Tumor Immune Estimation Resource (TIMER) databases. Then, we performed functional enrichment analysis of NUF2 co-expressed genes using R software and protein-protein interactions (PPIs) using the search tool used to retrieve interacting genes/proteins (STRING) databases.ResultsWe discovered that NUF2 mRNA expression was upregulated in ccRCC tissues and was associated with sex, grade, pathological stage, lymph node metastasis, and worse prognosis. In addition, NUF2 was positively linked to tumor immune cells in ccRCC. Moreover, NUF2 was closely related to genetic markers of different immune cells. Finally, functional enrichment and protein–protein interaction (PPI) analysis suggested that NUF2 and its closely related genes may be involved in the regulation of the cell cycle and mitosis. Our results suggested that NUF2 is correlated with a poor prognosis and immune infiltration in ccRCC.

Transcriptome mapping of renal clear cell carcinoma revealed by machine learning algorithm based on enhanced computed tomography images.

A growing number of studies have shown that inflammation-related components of the tumor microenvironment (TME) affect the clinical outcomes of cancer patients, and advances in radiomics may help predict survival and prognosis. We performed a systematic analysis of inflammation-related genes (IRGs) in clear cell renal cell carcinoma (ccRCC) from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus and mapped their interaction network to assess the specific relationship between these differentially expressed inflammation-related genes (DEIRGs) and inflammation. The association between DEIRGs and prognosis was discussed and further validated using consensus cluster analysis. Next, we constructed IRGs-related risk score from the collected information and validated the prognostic value of the model using Kaplan-Meier survival analysis and receiver operating characteristic analysis. Computed tomographic images corresponding to the TCGA-ccRCC cohort were obtained from the Cancer Imaging Archive database for radiomics signature extraction. We screened for prognostic IRGs and found that they were positively correlated with inflammatory cells in the tumor microenvironment associated with tumor progression and metastasis, such as activated CD8+ cells, myeloid-derived suppressor cells and neutrophils. The impact of IRGs on the prognosis of ccRCC patients was also verified. Using these differentially expressed genes, we successfully constructed a risk signature and validated its good prognosis assessment for patients. Furthermore, radiomics-based prognostic models performed better than those using risk signatures or clinical characteristics. IRG-related risk scores play an important role in assessing the prognosis and improving the management of patients with ccRCC. Through this feature, the infiltration of immune cells in the TME can be predicted. Furthermore, non-invasive radiomics signatures showed satisfactory performance in predicting ccRCC prognosis.

Construction and validation of programmed cell death-based molecular clusters for prognostic and therapeutic significance of clear cell renal cell carcinoma

As the dominant histological subtype of kidney cancer, clear cell renal cell carcinoma (ccRCC) poorly responds to conventional chemotherapy and radiotherapy. Although novel immunotherapies such as immune checkpoint inhibitors could have a durable effect in treating ccRCC patients, the limited availability of dependable biomarkers has restricted their application in clinic. In the study of carcinogenesis and cancer therapies, there has been a recent emphasis on researching programmed cell death (PCD). In the current study, we discovered the enriched and prognostic PCD in ccRCC utilizing gene set enrichment analysis (GSEA) and investigate the functional status of ccRCC patients with different PCD risks. Then, genes related to PCD that had prognostic value in ccRCC were identified for the conduction of non-negative matrix factorization to cluster ccRCC patients. Next, the tumor microenvironment, immunogenicity, and therapeutic response in different molecular clusters were analyzed. Among PCD, apoptosis and pyroptosis were enriched in ccRCC and correlated with prognosis. Patients with high PCD levels were related to poor prognosis and a rich but suppressive immune microenvironment. PCD-based molecular clusters were identified to differentiate the clinical status and prognosis of ccRCC. Moreover, the molecular cluster with high PCD levels may correlate with high immunogenicity and a favorable therapeutic response to ccRCC. Furthermore, a simplified PCD-based gene classifier was established to facilitate clinical application and used transcriptome sequencing data from clinical ccRCC samples to validate the applicability of the gene classifier. We thoroughly extended the understanding of PCD in ccRCC and constructed a PCD-based gene classifier for differentiation of the prognosis and therapeutic efficacy in ccRCC.

MMP9 and TYROBP affect the survival of circulating tumor cells in clear cell renal cell carcinoma by adapting to tumor immune microenvironment

Circulating tumor cells (CTCs) play a key role in tumor metastasis. CTCs have altered gene expression and can survive in the bloodstream. Finding the key genes whose expression are altered in CTCs could help explain the mechanism of tumor metastasis. We searched for genes differentially expressed in CTCs by analyzing four CTCs and primary tumor gene expression datasets in the GEO database. Key genes of clear cell renal cell carcinoma (ccRCC) CTCs were identified. The correlation between key genes and the immune microenvironment of ccRCC was explored. Finally, the CTCs cell model of ccRCC was constructed by in vivo screening method, and the expression of key genes was detected at the cell and tissue levels. A total of 771 DEGs were obtained. Gene enrichment analysis showed that DEGs of CTCs were mainly involved in the regulation of the tumor immune process and tumor cell apoptosis. Finally, we found 2 key genes, MMP9 and TYROBP in ccRCC CTCs. The high expression of these 2 genes predicted a poor prognosis of ccRCC, and the expression levels of these 2 genes were significantly increased in CTCs and ccRCC tissues. Our study suggested that genetic alterations in CTCs contribute to the ability of CTCs to survive in the blood by adapting to the tumor microenvironment. MMP9 and TYROBP are potential therapeutic and prognostic targets for ccRCC.

Significance of PBRM1 mutation in disease progress and drug selection in clear cell renal cell carcinoma

ABSTRACT Clear cell renal cell carcinoma (ccRCC) is the predominant type of kidney cancer, and the mutation of PBRM1 (Polybromo 1) gene is a commonly observed genetic alteration. The high frequency of PBRM1 mutation in ccRCC suggests its potential use as a biomarker for personalized therapy. In this study, we aimed to investigate the significance of PBRM1 mutation in disease progression and drug sensitivity in ccRCC. Additionally, we analyzed the critical pathways and genes associated with PBRM1 mutation to understand its potential mechanisms. Our findings show that PBRM1 mutation was observed in 38% of ccRCC patients and correlated with advanced disease stages. We also identified selective inhibitors for ccRCC with PBRM1 mutation using online databases such as PD173074 and AGI-6780. Furthermore, we identified 1253 genes as differentially expressed genes (DEGs) that were significantly enriched in categories such as metabolic progression, cell proliferation, and development. Although PBRM1 mutation did not show an association with ccRCC prognosis, a lower PBRM1 expression level correlated with worsened prognosis. Our study provides insights into the association of PBRM1 mutation with disease progression in ccRCC and suggests potential gene and signaling pathways for personalized treatment in ccRCC with PBRM1 mutation.

SPAG9 Expression Predicts Good Prognosis in Patients with Clear-Cell Renal Cell Carcinoma: A Bioinformatics Analysis with Experimental Validation.

Clear-cell renal cell carcinoma (ccRCC) is the most common and aggressive type of renal-cell carcinoma (RCC). Sperm-associated antigen 9 (SPAG9) has been reported to promote the progression of a variety of tumors and is thus a potential prognostic marker. This study combined a bioinformatics analysis with an experimental validation, exploring the prognostic value of SPAG9 expression in ccRCC patients and the possible underlying mechanisms. The SPAG9 expression was associated with a poor prognosis in pan-cancer patients, but with a good prognosis and slow tumor progression in ccRCC patients. To explore the underlying mechanism, we investigated the roles of SPAG9 in ccRCC and bladder urothelial carcinoma (BLCA). The latter was chosen for comparison with ccRCC to represent the tumor types in which SPAG9 expression suggests a poor prognosis. The overexpression of SPAG9 increased the expression of autophagy-related genes in 786-O cells but not in HTB-9 cells, and SPAG9 expression was significantly correlated with a weaker inflammatory response in ccRCC but not in BLCA. Through an integrated bioinformatics analysis, we screened out seven key genes (AKT3, MAPK8, PIK3CA, PIK3R3, SOS1, SOS2, and STAT5B) in this study. The correlation between SPAG9 expression and ccRCC prognosis depends on the expression of key genes. Since most of the key genes were PI3K-AKT-pathway members, we used the PI3K agonist 740Y-P to stimulate the 786-O cells, to mimic the effect of key-gene overexpression. Compared with the Ov-SPAG9 786-O cells, the 740Y-P further increased the expression of autophagy-related genes by more than twofold. Moreover, we constructed a nomogram based on SPAG9/key genes and other clinical features, which was proven to have some predictive value. Our study found that SPAG9 expression predicted opposite clinical outcomes in pan-cancer and ccRCC patients, and we speculated that SPAG9 suppresses tumor progression by promoting autophagy and inhibiting inflammatory responses in ccRCC. We further found that some genes might cooperate with SPAG9 to promote autophagy, and that these were highly expressed in the tumor stroma and could be represented by key genes. The SPAG9-based nomogram can help to estimate the long-term prognosis of ccRCC patients, indicating that SPAG9 is a potential prognostic marker for ccRCC.

SAA1 Has Potential as a Prognostic Biomarker Correlated with Cell Proliferation, Migration, and an Indicator for Immune Infiltration of Tumor Microenvironment in Clear Cell Renal Cell Carcinoma.

The tumor microenvironment (TME) plays an important part in the initiation and development of clear cell renal cell carcinoma (ccRCC). However, an understanding of the immune infiltration in TME is still unknown. Our study aims to explore the correlation between the TME and the clinical features, as well as the prognosis of ccRCC. In the present study, ESTIMATE and CIBERSORT computational methods were applied to calculate the proportion of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal fractions in the ccRCC form The Cancer Genome Atlas (TCGA) database. Then, we sought to find out those immune cell types and genes which may play a significant role and validated them in the GEO database. Furthermore, an immunohistochemical analysis of our external validation dataset was used to detect SAA1 and PDL1 expression in the ccRCC cancer tissues and corresponding normal tissues. Statistical analysis was performed to study the relationship between SAA1 and clinical characteristics, as well as PDL1 expression. Furthermore, a ccRCC cell model with SAA1 knockdown was constructed, which was used for cell proliferation and the migration test. The intersection analysis of the univariate COX and PPI analysis were performed to imply Serum Amyloid A1 (SAA1) as a predictive factor. The expression of SAA1 was significantly negatively correlated to OS and positively correlated to the clinical TMN stage system. The genes in the high-expression SAA1 group were basically enriched in immune-related activities. The proportion of mast cells resting was negatively correlated with SAA1 expression, indicating that SAA1 may be involved in the maintenance of the immune status for the TME. Moreover, the PDL1 expression was positively related to the SAA1 expression and negatively correlated with the patients' prognosis. Further experiments revealed that the knockdown of SAA1 inhibited ccRCC development through suppressing cell proliferation and migration. SAA1 may be a novel marker for the prognosis prediction of ccRCC patients and may play a vital role in the TME by mast cell resting and PDL1 expression. SAA1 has the potential to become a therapeutic target and indicator for immune target therapy in ccRCC treatment.

Comprehensive analysis of the roles of fatty acid transport related proteins in clear cell renal cell carcinoma

ObjectiveThis study aimed to explore the clinical significance of fatty acid transport-related protein (FATRP) in patients with clear cell renal cell carcinoma(ccRCC). MethodsRNA-seq data and corresponding clinical data of ccRCC were obtained from TCGA data portal. Seventeen key FATRP genes were comprehensively investigated using bioinformatics approaches to systematically investigate their expression patterns in ccRCC. In addition, the correlation between the expression levels of these genes and clinicopathological features in ccRCC was further explored. ResultsAmong the 17 key FATRP genes, only FABP5, FABP6, and FABP7 could be regarded as ideal biomarkers for ccRCC, as they were highly expressed in ccRCC tumor tissues, and positively correlates with tumor progression and poor prognosis. FABP6 had the highest copy number variations (CNV) events (63.07 %), and ccRCC patients with FABP6 amplification had a better prognosis than the unaltered group. DNA methylation levels of FABP6 and FABP7 were downregulated in ccRCC tumor tissues compared to those in normal tissues. FABP5 showed the opposite results. Moreover, a novel four FATRP gene (FABP1, FABP5, FABP7, FATP2) and three clinical parameter (age, stage, and grade) prediction model was constructed and that comprised a significant independent prognostic signature. ConclusionsOnly a few FATRP genes are upregulated in ccRCC tumor tissue, and positively correlate with tumor progression and poor prognosis. The accuracy of a single gene of these FATRP genes as predictors of progression and prognosis of ccRCC is limited. The performance of the novel prediction model proposed by this study was much better than that of any single gene.

Abstract 3127: Comprehensive proteogenomic characterization of rare kidney tumors

Abstract Non-clear cell renal cell carcinomas (non-ccRCCs) represent ~15-20% RCCs cases comprising nearly 20 different disease subtypes and a wide spectrum of clinical behavior from benign to highly aggressive course. Clinically, metastatic non-ccRCC patients, regardless of subtypes with distinct genomic aberrations, are all treated with the same standard of care therapies, underscoring the need for precision therapeutic strategies. Diagnostic challenges also exist as benign and malignant entities often display overlapping histomorphologies that current diagnostic cytokeratin markers cannot resolve. Therefore, identification of more reliable diagnostic and prognostic non-ccRCC biomarkers remains an unmet need in this field. As part of the Clinical Tumor Analysis Consortium (CPTAC), we performed integrative analysis of multi-omics data including genomic next generation sequencing-based whole exome, whole genome, RNAseq, snRNAseq and mass spectrometry-based proteomics, post translational modifications (glycosylation and phosphorylation) and metabolomic profiles generated by CPTAC. The composition of the kidney tumor cohort (n=151) included 103 ccRCC, 15 oncocytomas, 13 papillary RCC (PRCC), 11 other rare tumors and 8 unclassified RCCs. Our multi-omic analysis revealed both unique and shared molecular features of RCC subtypes. We characterized proteogenomic, PTM and glycoproteome impact of genome instability (GI), a feature that is associated with poor prognosis in both ccRCC and non-ccRCC and affects 10-15% of cases. These analyses identified new prognostic signatures, outlier targetable kinase expression patterns, kinase-substrate relationships and differential protein glycosylation events. Glycoproteome analysis also revealed variation in cell-type specific marker expression among RCC subtypes such as FUT8 (core-fucosyltransferase) associated protein glycosylation in PRCC. Integrative analysis of snRNA-seq data predicted diverse tumor cell-of-origin and stratified RCC subtype specific proteogenomic signatures. Differential expression analysis revealed several novel diagnostic makers including MAPRE3, GPNMB, PIGR, SOSTDC1. These biomarkers were validated by IHC and their addition to existing panels results in improved diagnostic specificity. Metabolic characterization revealed RCC subtype-specific differences and increased oncometabolite SAICAR in oncocytomas that may have functional significance. The valuable proteogenomic data resource we generated contains several rare tumor types that are hard to obtain for proteogenomic characterization at the scale described here, and will certainly aid in future pan-RCC studies. Citation Format: Ginny Xiaohe Li, Yi Hsiao, Lijun Chen, Rahul Mannan, Yuping Zhang, Francesca Petralia, Hanbyul Cho, Noshad Hosseini, Anna Calinawan, Yize Li, Shankara Anand, Aniket Dagar, Yifat Geffen, Felipe V. Leprevost, Anne Le, Sean Ponce, Michael Schnaubelt, Nataly Naser Al Deen, Wagma Caravan, Andrew Houston, Chandan Kumar-Sinha, Xiaoming Wang, Seema Chugh, Gilbert S. Omenn, Daniel W. Chan, Christopher Ricketts, Rohit Mehra, Arul Chinnaiyan, Li Ding, Marcin Cieslik, Hui Zhang, Saravana M. Dhanasekaran, Alexey I. Nesvizhskii. Comprehensive proteogenomic characterization of rare kidney tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3127.

Comprehensive analysis of prognostic value, immune implication and biological function of CPNE1 in clear cell renal cell carcinoma.

Background: Elevated expression of Copine-1 (CPNE1) has been proved in various cancers; however, the underlying mechanisms by which it affects clear cell renal cell carcinoma (ccRCC) are unclear. Methods: In this study, we applied multiple bioinformatic databases to analyze the expression and clinical significance of CPNE1 in ccRCC. Co-expression analysis and functional enrichment analysis were investigated by LinkedOmics, cBioPortal and Metascape. The relationships between CPNE1 and tumor immunology were explored using ESTIMATE and CIBERSORT method. In vitro experiments, CCK-8, wound healing, transwell assays and western blotting were conducted to investigate the effects of gain- or loss-of-function of CPNE1 in ccRCC cells. Results: The expression of CPNE1 was notably elevated in ccRCC tissues and cells, and significantly correlated with grade, invasion range, stage and distant metastasis. Kaplan-Meier and Cox regression analysis displayed that CPNE1 expression was an independent prognostic factor for ccRCC patients. Functional enrichment analysis revealed that CPNE1 and its co-expressed genes mainly regulated cancer-related and immune-related pathways. Immune correlation analysis showed that CPNE1 expression was significantly related to immune and estimate scores. CPNE1 expression was positively related to higher infiltrations of immune cells, such as CD8+ T cells, plasma cells and regulatory T cells, exhibited lower infiltrations of neutrophils. Meanwhile, elevated expression of CPNE1 was characterized by high immune infiltration levels, increased expression levels of CD8+ T cell exhaustion markers (CTLA4, PDCD1 and LAG3) and worse response to immunotherapy. In vitro functional studies demonstrated that CPNE1 promoted proliferation, migration and invasion of ccRCC cells through EGFR/STAT3 pathway. Conclusion: CPNE1 is a reliable clinical predictor for the prognosis of ccRCC and promotes proliferation and migration by activating EGFR/STAT3 signaling. Moreover, CPNE1 significantly correlates with immune infiltration in ccRCC.

Comprehensive analysis of alternative polyadenylation regulators of CTLA4 and immune infiltration in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer. Alternative polyadenylation (APA) plays an important role in the progression and immunity of multiple tumors. Although immunotherapy has emerged as an important treatment option for metastatic renal cell carcinoma, whether APA affects the tumor immune microenvironment (TIME) in ccRCC remains unclear. Patients with ccRCC were classified into two groups by performing a consensus clustering analysis of APA factor expression profiles. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to assess the association between APA regulators and ccRCC prognosis. Through the use of the R package, GSVA, the correlation between SNRNP70 expression and tumor immune features were analyzed. The TCGA data revealed that APA regulators were associated with Cytotoxic T-Lymphocyte Associated Protein 4 (CTLA4) expression. Cluster 1 exhibited a higher grade and histological tumor stage, as well as a worse prognosis compared to Cluster 2. A ssGSEA analysis demonstrated that Cluster 2 possessed an extensively higher level of immune infiltration. Moreover, high SNRNP70 expression was found to be positively correlated with CTLA4 expression and a poor prognosis in ccRCC. Thus, SNRNP70 might represent a novel immune-related prognostic biomarker in ccRCC. A pan-cancer analysis suggested that SNRNP70 may also play a role in other types of cancer by affecting the TIME. The data from this study indicate that APA regulators play a key role in immune infiltration in ccRCC. SNRNP70 is a promising prognostic biomarker and a potential target for ccRCC's immunotherapy.

MPP7 is a potential prognostic marker and is associated with cancer metabolism and immune infiltration in clear cell renal cell carcinoma: a bioinformatics analysis based on the TCGA database.

Metastasis is a major negative prognostic marker in clear cell renal cell carcinoma (ccRCC). Membrane palmitoylated proteins (MPPs) are a class of cell polarity-associated proteins that function in both cell-cell junction and adhesion. However, the relationship between MPP7 and the prognosis of ccRCC remains elusive. In this study, we aimed to investigate the associations between MPP7 expression with clinical prognosis of ccRCC using bioinformatics analyses. The messenger RNA (mRNA) and protein expression patterns of MPP7 in different cancer types were examined using The Cancer Genome Atlas (TCGA) and Human Protein Atlas (HPA) databases, with key clinical characteristics (TNM and pathological stages, pathological grade, survival status) included. A nomogram model using MPP7 expressions and other clinical factors was built to predict the survival probability. The Kaplan-Meier plotter and Cox regression were employed to investigate the clinical significance and prognostic value of MPP7 in ccRCC. MPP7 expression-associated signaling pathways with were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) tools. The Tumor Immune Estimation Resource (TIMER) database was used to investigate the correlation between MPP7 and the infiltration patterns of immune cells. By analyzing TCGA-kidney renal clear cell carcinoma (TCGA-KIRC) and HPA databases, we found that MPP7 was differentially expressed in tumor tissues and adjacent normal tissues (P<0.001). The MPP7 expression patterns were associated with pathological stage (P<0.001), histological grade (P<0.01), and survival status (P<0.001). Using nomogram model, Cox regression and survival analysis, it showed that MPP7 expressions combined with key clinical factors could accurately predict the clinical prognosis. The promoter methylation patterns of MPP7 were correlated with the clinical factors of ccRCC patients. Furthermore, the KEGG and GO analyses demonstrated that MPP7 is associated with mitochondrial oxidative metabolism. MPP7 expression was associated with multiple types of immune cells and correlated with the enrichment of these cells. MPP7 is a critical gene links with ccRCC prognosis and is associated with tumor immune status and metabolism. MPP7 could become a potential biomarker and important therapeutic target for ccRCC patients.

Development and validation of a novel 5 cuproptosis-related long noncoding RNA signature to predict diagnosis, prognosis, and drug therapy in clear cell renal cell carcinoma.

Cuproptosis has been reported as a new form of cell death. However, its potential mechanism of action in clear cell renal cell carcinoma (ccRCC) remains unclear. Therefore, we systematically clarified the role of cuproptosis in ccRCC and aimed to develop a novel signature of cuproptosis-related long noncoding RNAs (lncRNA) (CRLs) to assess the clinical characteristics of ccRCC patients. Gene expression, copy number variation, gene mutation, and clinical data for ccRCC were obtained from The Cancer Genome Atlas (TCGA). CRL signature was constructed with least absolute shrinkage and selection operator (LASSO) regression analysis. The clinical diagnostic value of the signature was verified by clinical data. The prognostic value of the signature was detected by Kaplan-Meier analysis and receiver operating characteristic (ROC) curve. The prognostic value of the nomogram was evaluated by calibration curves, ROC curves, and decision curve analysis (DCA). Gene set enrichment analysis (GSEA), single sample GSEA (ssGSEA) and cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm were used to analyze the differences of immune function and immune cell infiltration among different risk groups. Prediction of clinical treatment differences in populations with different risks and susceptibilities was completed with R package (The R Foundation of Statistical Computing). Verification of key lncRNA expression was performed by quantitative real-time polymerase chain reaction (qRT-PCR). The cuproptosis-related genes were extensively dysregulated in ccRCC. A total of 153 differentially expressed prognostic CRLs were identified in ccRCC. Furthermore, a 5-lncRNA signature (AC015912.3, AC026401.3, AC103706.1, AC134312.5, and EMX2OS) were obtained that showed good performance in the diagnosis and prognosis of ccRCC. The nomogram could more accurately predict overall survival (OS). Immune functions such as T-cell and B-cell receptor signaling pathways showed differences between different risk groups. Clinical treatment value analysis showed that the signature may be able to effectively guide immunotherapy and target therapy. In addition, qRT-PCR results showed significant differences in the expression of key lncRNAs in ccRCC. Cuproptosis plays an important role in the progression of ccRCC. The 5-CRL signature can guide the prediction of clinical characteristics and tumor immune microenvironment of ccRCC patients.

Construction and validation of a prognostic model for predicting clear cell renal cell carcinoma based on complement-related genes.

Clear cell renal cell carcinoma (ccRCC) is a highly heterogeneous tumor and is the most common subtype of renal cell carcinoma (RCC). Surgery is used to cure most early ccRCC, but the 5-year overall survival (OS) of ccRCC patients is far from satisfactory. Thus, new prognostic features and therapeutic targets for ccRCC need to be identified. Since complement factors can influence tumor development, we aimed to develop a model to predict the prognosis of ccRCC through complement-related genes. Differentially expressed genes were screened from an International Cancer Genome Consortium (ICGC) data set, and the genes associated with prognosis were screened by univariate regression and least absolute shrinkage and selection operator-Cox regression, and column line plots were generated using the rms R package to predict OS. The C-index was used to show the accuracy of the survival prediction and the prediction effects were verified using a data set from The Cancer Genome Atlas (TCGA). An immuno-infiltration analysis was performed with CIBERSORT analysis, and a drug sensitivity analysis was performed using the Gene Set Cancer Analysis (GSCA) (http://bioinfo.life.hust.edu.cn/GSCA/#/) database. We identified 5 complement-related genes (i.e., A2M, APOBEC3G, COL4A2, DOCK4, and NOTCH4) for risk-score modeling to predict OS at 1, 2, 3, and 5 years, and the C-index of the prediction mode was 0.795. In addition, the model was successfully validated in TCGA data set. The CIBERSORT analysis showed that M1 macrophages were downregulated in the high-risk group. The GSCA database analysis showed that DOCK4, COL4A2, and A2M were positively correlated with the half maximal inhibitory concentration (IC50) of 10 drugs and small molecules, and COL4A2, NOTCH4, A2M, and APOBEC3G were negatively correlated with the IC50 of dozens of different drugs and small molecules. We developed and validated a survival prognostic model based on 5 complement-related genes for ccRCC. We also elucidated the relationship with tumor immune status and developed a new predictive tool for clinical purposes. In addition, our results showed that A2M, APOBEC3G, COL4A2, DOCK4, and NOTCH4 may be potential targets for the treatment of ccRCC in the future.

Low expression of SLC34A1 is associated with poor prognosis in clear cell renal cell carcinoma

ObjectiveClear cell renal cell carcinoma (ccRCC) is a malignant renal tumor that is highly prone to metastasis and recurrence. The exact pathogenesis of this cancer is still not well understood. This study aimed to identify novel hub genes in renal clear cell carcinoma and determine their diagnostic and prognostic value.MethodsIntersection genes were obtained from multiple databases, and protein–protein interaction analysis and functional enrichment analysis were performed to identify key pathways related to the intersection genes. Hub genes were identified using the cytoHubba plugin in Cytoscape. GEPIA and UALCAN were utilized to observe differences in mRNA and protein expression of hub genes between KIRC and adjacent normal tissues. The Wilcoxon rank sum test was used to analyze hub gene levels between paired KIRC and matched non-cancer samples. IHC results were obtained from the HPA online database, and according to the median gene expression level, they were divided into a high-expression group and a low-expression group. The correlation of these groups with the prognosis of KIRC patients was analyzed. Logistic regression and the Wilcoxon rank sum test were used to test the relationship between SLC34A1 level and clinicopathological features. The diagnostic value of SLC34A1 was evaluated by drawing the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). Cox regression analysis was used to analyze the relationship between clinicopathological features, SLC34A1 expression, and KIRC survival rate. LinkedOmics was used to obtain the genes most related to SLC34A1 and their functional enrichment. Genetic mutations and methylation levels of SLC34A1 in KIRC were obtained from the cBioPortal website and the MethSurv website, respectively.ResultsFifty-eight ccRCC differential genes were identified from six datasets, and they were mainly enriched in 10 functional items and 4 pathways. A total of 5 hub genes were identified. According to the GEPIA database analysis, low expression of SLC34A1, CASR, and ALDOB in tumors led to poor prognosis. Low expression of SLC34A1 mRNA was found to be related to clinicopathological features of patients. SLC34A1 expression in normal tissues could accurately identify tumors (AUC 0.776). SLC34A1 was also found to be an independent predictor of ccRCC in univariate and multivariate Cox analyses. The mutation rate of the SLC34A1 gene was 13%. Eight of the 10 DNA methylated CpG sites were associated with the prognosis of ccRCC. SLC34A1 expression in ccRCC was positively correlated with B cells, eosinophils, neutrophils, T cells, TFH, and Th17 cells, and negatively correlated with Tem, Tgd, and Th2 cells.ConclusionThe expression level of SLC34A1 in KIRC samples was found to be decreased, which predicted a decreased survival rate of KIRC. SLC34A1 may serve as a molecular prognostic marker and therapeutic target for KIRC patients.

Establishment and validation of a novel anoikis-related prognostic signature of clear cell renal cell carcinoma.

Despite progression in its treatment, the clinical outcome of patients with clear cell renal cell carcinoma (ccRCC) remains not ideal. Anoikis is a unique form of programmed apoptosis, owing to insufficient cell-matrix interactions. Anoikis plays a crucial role in tumor migration and invasion, and tumor cells could protect themselves through the capacity of anoikis resistance. Anoikis-related genes (ARGs) were obtained from Genecards and Harmonizome portals. The ARGs related to ccRCC prognosis were identified through univariate Cox regression analysis, then we utilized these ARGs to construct a novel prognostic model for ccRCC patients. Moreover, we explored the expression profile of ARGs in ccRCC using the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. We also conducted Real-Time Polymerase Chain Reaction (RT-PCR) to probe ARGs expression of the risk score. Finally, we performed correlation analysis between ARGs and tumor immune microenvironment. We identified 17 ARGs associated with ccRCC survival, from which 7 genes were chosen to construct a prognostic model. The prognostic model was verified as an independent prognostic indicator. The expression of most ARGs was higher in ccRCC samples. These ARGs were closely correlated with immune cell infiltration and immune checkpoint members, and had independent prognostic value respectively. Functional enrichment analysis demonstrated that these ARGs were significantly associated with multiple types of malignances. The prognostic signature was identified to be highly efficient in predicting ccRCC prognosis, and these ARGs were closely related to tumor microenvironment.

Integrative analysis of transcriptomic landscape and urinary signature reveals prognostic biomarkers for clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) patients with venous tumor thrombus (VTT) have poor prognosis. We aimed to reveal features of ccRCC with VTT and develop a urine-based prognostic classifier to predict ccRCC prognosis through integrative analyses of transcriptomic landscape and urinary signature. RNA sequencing was performed in five patients with ccRCC thrombus-tumor-normal tissue triples, while mass spectrometry was performed for urine samples from 12 ccRCC and 11 healthy controls. A urine-based classifier consisting of three proteins was developed to predict patients' survival and validated in an independent cohort. Transcriptomic analysis identified 856 invasion-associated differentially expressed genes (DEGs). Furthermore, proteomic analysis showed 133 differentially expressed proteins (DEPs). Integration of transcriptomic landscape and urinary signature reveals 6 urinary detectable proteins (VSIG4, C3, GAL3ST1, TGFBI, AKR1C3, P4HB) displaying abundance changes consistent with corresponding genes in transcriptomic profiling. According to TCGA database, VSIG4, TGFBI, and P4HB were significantly overexpressed in patients with shorter survival and might be independent prognostic factors for ccRCC (all p<0.05). A prognostic classifier consisting of the three DEPs highly associated with survival performed satisfactorily in predicting overall survival (HR=2.0, p<0.01) and disease-free survival (HR=1.6, p<0.001) of ccRCC patients. The ELISA analysis of urine samples from an independent cohort confirmed the satisfied predictive power of the classifier for pathological grade (AUC=0.795, p<0.001) and stage (AUC=0.894, p<0.001). Based on integrative analyses of transcriptomic landscape and urinary signature, the urine-based prognostic classifier consisting of VSIG4, TGFBI, and P4HB has satisfied predictive power of ccRCC prognosis and may facilitate ccRCC molecular subtyping and treatment.

An immune infiltration-related prognostic model of kidney renal clear cell carcinoma with two valuable markers: CAPN12 and MSC.

Since its discovery, clear cell renal cell carcinoma (ccRCC) has been the most prevalent and lethal kidney malignancy. Our research aims to identify possible prognostic genes of ccRCC and to develop efficient prognostic models for ccRCC patients based on multi-omics investigations to shed light on the treatment and prognosis of ccRCC. To determine a risk score for each patient, we screened out differentially expressed genes using data from tumor samples, and control samples mined from The Cancer Genome Atlas (TCGA) and GTEx datasets. Somatic mutation and copy number variation profiles were analyzed to look for specific genomic changes connected to risk scores. To investigate potential functional relationships of prognostic genes, gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were carried out. We created a prognostic model by fusing risk ratings with other clinical variables. For validation, the 786-O cell line was used to carry out the dual-gRNA approach to knock down CAPN12 and MSC. This was followed by qRT-PCR to verify the knockdown of CAPN12 and MSC. For ccRCC, seven predictive genes were discovered: PVT1, MSC, ALDH6A1, TRIB3, QRFPR, CYS1, and CAPN12. The most enriched pathways in the GSVA study and GSEA analysis promote tumorigenesis and immune system modulation. The risk score derived from prognostic genes corresponds with immune infiltration cells and helps predict how well a medicine will work. The mutation of numerous oncogenes was also linked to a high-risk score. A prognostic model with a high ROC value was created for the risk score. An in vitro study demonstrates that the suppression of CAPN12 and MSC dramatically reduced the ability of 786-O cells to proliferate in the CCK-8 proliferation assay and plate clonality assays. A thorough prognostic model with good performance has been developed for ccRCC patients using seven prognostic genes that were discovered to be related to ccRCC prognosis. In ccRCC, CAPN12 and MSC were significant indicators and would make good therapeutic targets.

A novel 7-chemokine-genes predictive signature for prognosis and therapeutic response in renal clear cell carcinoma.

Background: Renal clear cell carcinoma (ccRCC) is one of the most prevailing type of malignancies, which is affected by chemokines. Chemokines can form a local network to regulate the movement of immune cells and are essential for tumor proliferation and metastasis as well as for the interaction between tumor cells and mesenchymal cells. Establishing a chemokine genes signature to assess prognosis and therapy responsiveness in ccRCC is the goal of this effort. Methods: mRNA sequencing data and clinicopathological data on 526 individuals with ccRCC were gathered from the The Cancer Genome Atlas database for this investigation (263 training group samples and 263 validation group samples). Utilizing the LASSO algorithm in conjunction with univariate Cox analysis, the gene signature was constructed. The Gene Expression Omnibus (GEO) database provided the single cell RNA sequencing (scRNA-seq) data, and the R package "Seurat" was applied to analyze the scRNA-seq data. In addition, the enrichment scores of 28 immune cells in the tumor microenvironment (TME) were calculated using the "ssGSEA" algorithm. In order to develop possible medications for patients with high-risk ccRCC, the "pRRophetic" package is employed. Results: High-risk patients had lower overall survival in this model for predicting prognosis, which was supported by the validation cohort. In both cohorts, it served as an independent prognostic factor. Annotation of the predicted signature's biological function revealed that it was correlated with immune-related pathways, and the riskscore was positively correlated with immune cell infiltration and several immune checkpoints (ICs), including CD47, PDCD1, TIGIT, and LAG-3, while it was negatively correlated with TNFRSF14. The CXCL2, CXCL12, and CX3CL1 genes of this signature were shown to be significantly expressed in monocytes and cancer cells, according to scRNA-seq analysis. Furthermore, the high expression of CD47 in cancer cells suggested us that this could be a promising immune checkpoint. For patients who had high riskscore, we predicted 12 potential medications. Conclusion: Overall, our findings show that a putative 7-chemokine-gene signature might predict a patient's prognosis for ccRCC and reflect the disease's complicated immunological environment. Additionally, it offers suggestions on how to treat ccRCC using precision treatment and focused risk assessment.